Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2006

Abstract

In many surveys, missing response is a common problem. As an example, Zahner, Jacobs, Freeman and Trainor analysed data from a study of child psychopathology in the State of Connecticut, USA. In that study, the response variable, psychopathology, was inferred from questions that were addressed to teachers of the children and was subject to a high level of missingness. However, the missing responses were supplemented by surrogate information that was provided by the parents and/or the primary care providers of the children. In such a situation, it is conceivable that the supplemental information can be used to recover some of the information that has been lost in the cases with missing response. This paper considers a method using empirical likelihood. Empirical likelihood is well known in providing nonpara-metric inference. But its application has largely been confined to complete-data situations. The method proposed exploits the semiparametric nature of empirical likelihood. The method gives consistent estimates if the cases with non-missing responses form a random sample of the population. In large samples, the method behaves similarly to a regression estimate that is applied to estimating equations. The method is easy to implement with standard statistical packages. In a small sample study, the method was found to give favourable results, when compared with existing methods.

Keywords

Auxiliary information, Empirical likelihood, Missing values, Surrogate, Survey

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of the Royal Statistical Society - Series C: Applied Statistics

Volume

55

Issue

3

First Page

379

Last Page

396

ISSN

0035-9254

Identifier

10.1111/j.1467-9876.2006.00542.x

Publisher

Royal Statistical Society

Additional URL

https://doi.org/10.1111/j.1467-9876.2006.00542.x

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